Useful advice on controlling the False Discovery Rate (FDR)!

Massive increases in the amount of data scientists are able to acquire and analyze over the past two decades have driven the development of new statistical tools that can better deal with the challenges of “big data.” One such set of tools is ways of controlling the “false discovery rate” (FDR) in a set of statistical tests. FDR is simply the mean proportion of statistically significant test results that are really false positives. As you may recall from your introductory statistics course, when you perform multiple statistical tests, the probability of false positive results rapidly increases. For example, if one were to perform a single test using an alpha level of 5% and there truly is no effect of the factor being tested, there is only a 5% chance of a false positive result. However, if one were to perform 10 tests using an alpha level of 5% for each…

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